Web Browser Based Data Visualization Scheme for XBee Wireless Sensor Network

Authors

  • Xinzhou Wei Department of Electrical & Telecommunications Engineering Technology New York City College of Technology The City University of New York
  • Li Geng Department of Electrical and Telecommunications Engineering Technology, New York City College of Technology, City University of New York, 300 Jay St, Brooklyn, NY, U.S.A.
  • Xiaowen Zhang Department of Computer Science, College of Staten Island, City University of New York 2800 Victory Blvd., Staten Island, NY 10314, U.S.A.

DOI:

https://doi.org/10.14738/tnc.65.5261

Keywords:

Data visualization, Internet of Things, Smart building, Wireless sensor network, XBee, ZigBee.

Abstract

Wireless sensor network (WSN) plays an important role in the infrastructure of Internet of Things (IoT). Data visualization is an essential component in WSN to facilitate data scientists to interpret information clearly and efficiently.  In this paper, we conduct a study of XBee based WSN which integrates the DASH data visualization scheme for building a web-browser based application without using HTML or JavaScript. The data collected from wireless sensors in a WSN were displayed in a web browser with interactive functions. The proposed visualization scheme is real-time, cross platform, and hardware independent. Thus, it could be easily employed on any operating system. Experimental results demonstrated that our WSN data visualization scheme using XBee Python package and Plotly's DASH is feasible for IoT applications like smart buildings, environment monitoring, as well as other WSN applications.

Author Biographies

Xinzhou Wei, Department of Electrical & Telecommunications Engineering Technology New York City College of Technology The City University of New York

Xinzhou Wei is an Associate Professor in the Department of Electrical and Telecommunication Engineering Technology of the New York City College of Technology (CityTech) of The City University of New York (CUNY). He received his B.S. and M.S. degree from Xi'an Jiaotong University in 1988 and 1991 respectively. In 2002, he received his Ph.D. degree in Computer Science from the Graduate Center of CUNY. His research focuses on computer network security, telecommunication, and image processing. Currently he is an IEEE senior member and IEEE student branch faculty advisor at CityTech.

Li Geng, Department of Electrical and Telecommunications Engineering Technology, New York City College of Technology, City University of New York, 300 Jay St, Brooklyn, NY, U.S.A.

Li Geng is currently an Assistant Professor in Department of Electrical Engineering& Telecommunication Technology at New York City College of Technology of the City University of New York. She received her B. S. degree in Telecommunication Engineering from Huazhong University of Science and Technology, Wuhan, China, in 2009. She received her Ph.D. degree in Electrical Engineering from Stony Brook University, New York, USA, in 2015. Her research interests are in the area of statistical signal processing, signal detection and estimation, big data analytics, as well as their applications to a wide area such as indoor positioning in the Internet of Things.

Xiaowen Zhang, Department of Computer Science, College of Staten Island, City University of New York 2800 Victory Blvd., Staten Island, NY 10314, U.S.A.

Xiaowen Zhang is an Associate Professor in the Department of Computer Science at College of Staten Island and a doctoral faculty member in Ph.D. Program in Computer Science at CUNY Graduate Center. He received a Ph.D. in Computer Science from CUNY and a Ph.D. in Electrical Engineering from Beijing Jiaotong University. His research interests include information security, cryptography, quantum computing, wireless communications, biometrics, and RFID security and privacy. At CUNY Graduate Center, he has supervised three Ph.D. dissertations. He is currently supervising two Ph.D. students. He is a member of ACM and IEEE.

References

(1) Minoli D, Sohraby K, Occhiogrosso, B. IoT considerations, requirements, and architectures for smart buildings—Energy optimization and next-generation building management systems. IEEE Internet of Things, 2017, 4(1): 269-283.

(2) Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M. Internet of Things for smart cities. IEEE Internet of Things, 2014, pp. 22-32.

(3) Mitton N, Papavassiliou S, Puliafito A, Trived K S. Combining cloud and sensors in a smart city environment. EURASIP Journal on Wireless Communications and Networking, 2012, p. 247.

(4) Ghayvat H, Mukhopadhyay S, Gui X, Suryadevara N. WSN- and IOT-based smart homes and their extension to smart buildings. Sensors, 15(5): 10350–10379.

(5) Jang W S, Healy W M, Skibniewski M J. Wireless sensor networks as a part of a web-based building environmental monitoring system. Automation in Construction, 2008, 17(6):729-736.

(6) Liu X, Chen H, Wang M, Chen S. An XBee-Pro Based Energy Monitoring System. Brisbane, Australasian Telecommunication Networks and Applications Conference (ATNAC), 2012, pp. 1-6.

(7) Othman S B, Trad A, Youssef H. Security architecture for at-home medical care using wireless sensor network.. International Wireless Communications and Mobile Computing Conference (IWCMC), 2014, pp. 304-309.

(8) Davcev D, Mitreski K, Trajkovic S, Nikolovski V, and Koteli N. IoT agriculture system based on LoRaWAN. 14th IEEE International Workshop on Factory Communication Systems (WFCS). 2018.

(9) Shamas N. Why data visualization is important. TechChange, May 19, 2015. https://www.techchange.org/2015/05/19/data-visualization-analysis-international-development/.

(10) Link A. Why create visualizations of your data? Data Visualization, 2017. https://dash.umn.edu/data-visualization/.

(11) Levis P, Lee N, Welsh M, Culler D. TOSSIM: accurate and scalable simulation of entire TinyOS applications. In Proc. the 1st International Conference on Embedded Networked Sensor Systems, 2003, pp. 126-137.

(12) Buschmann C, Pfisterer D, Fischer S, Fekete S P, Kroller A. SpyGlass: a wireless sensor network visualizer. ACM SIGBED Review, 2005, 2(1): 1-6.

(13) Tuton M. MOTE VIEW: A sensor network monigoring and management tool. Second IEEE Workshop on Embedded Networked Sensors (EmNetS-II), 2005, pp. 11-18.

(14) Leonov A. MeshNetics demonstrated integration of wireless sensor data with SCADA system at ZigBee open house. CISION - PRWeb, June 2006. https://www.prweb.com/releases/2006/06/prweb403245.htm.

(15) Pinto J, Sousa A, Goncalves G M, Lebres P, Sousa J. MonSense-application for deployment, monitoring and control of wireless sensor networks. ACM Real Wireless Sensor Network Conference, 2006.

(16) Shu L, Wu C, Zhang Y, Chen J, Wang L, Hauswirth M. NetTopo: Beyond Simulator and Visualizer for Wireless Sensor Networks. IEEE Second International Conference on Future Generation Communication and Networking, 2008, pp. 17-20.

(17) Castillo J A, Ortiz A M, Lopez V, Olivares T, Orozco-Barbosa L. WiseObserver: a real experience with wireless sensor networks. In Proc. the 3nd ACM workshop on Performance Monitoring and Measurement of Heterogeneous Wireless and Wired Networks, 2008, pp. 23-26.

(18) Jurdak R, Ruzzelli A G, Barbirato A, Boivineau S. Octopus: monitoring, visualization, and control of sensor networks. Wireless Communications & Mobile Computing, 2011, 11: 1073-1091.

(19) Surge Network Viewer. By Crossbow Technology, Inc. http://www.hoskin.qc.ca/uploadpdf/ Instrumentation/divers/CrossBow/divers_Surge%20Network%20Viewer_4271286a0135f.pdf.

(20) Parbat B, Dwivedi A K, Vyas O.P. Data visualization tools for WSNs: A glimpse. International Journal of Computer Applications, 2010, 2(1): 14-20.

(21) dAuriol B J, Lee S, Lee Y. K. Visualizations of Wireless Sensor Network Data. In Handbook of Research on Developments and Trends in Wireless Sensor Networks: From Principle to Practice. Hershey : IGI Global, 2010, Chapter 16, pp. 353-370.

(22) ElHakim R, ElHelw M. Interactive 3D visualization for wireless sensor networks. 6-8, June 2010, The Visual Computer, 2010, Vol. 26, Issue 6-8, pp. 1071-1077.

(23) Ravichandranb S, Chandrasekarb R K, Uluagac A S, Beyah R. A simple visualization and programming framework for wireless sensor networks: PROVIZ. Ad Hoc Networks, 2016, 53: 1-16.

(24) Moffitt C. Overview of Python visualization tools. Practical Business Python, 2015. http://pbpython.com/visualization-tools-1.html.

(25) Moffit C, Choosing a Python visualization tool. Practical Business Python, 2018. http://pbpython.com/python-vis-flowchart.html.

(26) Moffitt, C. Creating interactive visualizations with Plotly's Dash framework. Practical Business Python, 2017. http://pbpython.com/plotly-dash-intro.html.

(27) Kinsley H. Introduction to data visualization applications with Dash and Python. Pythonprogramming.net, 2018. https://pythonprogramming.net/data-visualization-application-dash-python-tutorial-introduction/.

(28) Faludi R. Building Wireless Sensor Networks: with ZigBee, XBee, Arduino, and Processing. O'Reilly Media, 2010.

(29) Wei X, Geng L, Zhang X. An open source data visualization system for wireless sensor network. Journal of Computer Science and Information Technology, 2017,5(2): 10-17.

(30) Parmer C. Introduction to DASH. Build beautiful web-based interfaces in Python. 2016. https://dash.plot.ly/introduction.

Downloads

Additional Files

Published

2018-11-04

How to Cite

Wei, X., Geng, L., & Zhang, X. (2018). Web Browser Based Data Visualization Scheme for XBee Wireless Sensor Network. Discoveries in Agriculture and Food Sciences, 6(5), 59. https://doi.org/10.14738/tnc.65.5261

Issue

Vol. 6 No. 5 (2018): Transactions on Networks and Communications

Section

Articles